Speaker: Feng Qiu, Argonne National Laboratory
Title: Decomposing Transmission Constraints for Decentralized Power System Optimization
Abstract: One of the most complicating factors in decentralized optimization for power systems is the modeling of power flow equations. Existing formulations for direct current (or DC) power flows either have limited scalability or are very dense and unstructured, making them unsuitable for large-scale decentralized studies. In this work, we present a novel DC power flow formulation, based on sparsified injection shift factors, which has a decomposable block-diagonal structure, scales well for large systems, and can efficiently handle N-1 security requirements. Benchmarks on Multi-Zonal Security-Constrained Unit Commitment problems show that the proposed formulation can reliably and efficiently solve instances with up to 6,515 buses, with no convergence or numerical issues.
Biography: Feng Qiu received his Ph.D. from the School of Industrial and Systems Engineering at the Georgia Institute of Technology in 2013. He is a principal computational scientist and a section leader with the Energy Systems Division at Argonne National Laboratory. His current research interests include power system modeling and optimization, electricity markets, power grid resilience, machine learning and data analytics.
Webinar Link: https://iastate.zoom.us/j/91974478792